Case Study

Optimising chemical experiment design

Use AI to overcome inefficiencies in experiment design and make
the production process both faster and more efficient. 

reduction in material costs and chemist time.


A large chemical enterprise was looking to optimise the configuration of the production process used to manufacture a new product. The aim was to maximise the quantity of the product produced while minimising the cost of the production and preserving the form properties of the product.

Due to the complexity of the process, it was challenging to predict the result of combinations of parameters in the production, and each trial of a new configuration for the production process was both costly and time-consuming. A systematic approach to this optimisation was required to make it both quicker and more efficient. The product itself was also costly and time-consuming to manufacture, meaning that the iteration count needed to be minimised as much as possible.


There were many previous examples of configurations in similar production processes that the customer was interested in. These previous experiments highlighted significant nonlinearities in the combined behaviour of the complex system, which made it extremely difficult to infer the next best configuration to trial in order to balance the exploitation of promising areas, but also the exploration of large areas of uncertainty towards the extremities of each parameter.

Working with the customer using the Mind Foundry Platform, we were able to effectively encode the configuration space and constraints of the problem and define a scoring function that encapsulates all quantities of interest.


Concurrently seeding the Platform with existing information from similar production processes quickly led to significantly improved results in the process and in far fewer iterations than would have otherwise been required. This saved a substantial amount of time and money for the client, and they achieved:

  • 90% reduction in material costs and chemist time spent.

  • Intelligent parameterisation of the chemical process.

  • Efficient optimisation of the reaction configuration to minimise experimentation time.

  • Cost reduced from fewer failed experiments.

  • Faster discovery of new materials.

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